Feature filter for estimating central mean subspace and its sparse solution
Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Pei [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
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2021transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center - Phillips, Eileen ELSEVIER, 2014, Amsterdam |
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| Übergeordnetes Werk: |
volume:163 ; year:2021 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.csda.2021.107285 |
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| 520 | |a Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. | ||
| 520 | |a Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. | ||
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10.1016/j.csda.2021.107285 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001445.pica (DE-627)ELV054566851 (ELSEVIER)S0167-9473(21)00119-5 DE-627 ger DE-627 rakwb eng 610 VZ 540 VZ 35.18 bkl Wang, Pei verfasserin aut Feature filter for estimating central mean subspace and its sparse solution 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. Characteristic function Elsevier Central mean subspace Elsevier Sufficient dimension reduction Elsevier Feature filter Elsevier Yin, Xiangrong oth Yuan, Qingcong oth Kryscio, Richard oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:163 year:2021 pages:0 https://doi.org/10.1016/j.csda.2021.107285 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 163 2021 0 |
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10.1016/j.csda.2021.107285 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001445.pica (DE-627)ELV054566851 (ELSEVIER)S0167-9473(21)00119-5 DE-627 ger DE-627 rakwb eng 610 VZ 540 VZ 35.18 bkl Wang, Pei verfasserin aut Feature filter for estimating central mean subspace and its sparse solution 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. Characteristic function Elsevier Central mean subspace Elsevier Sufficient dimension reduction Elsevier Feature filter Elsevier Yin, Xiangrong oth Yuan, Qingcong oth Kryscio, Richard oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:163 year:2021 pages:0 https://doi.org/10.1016/j.csda.2021.107285 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 163 2021 0 |
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10.1016/j.csda.2021.107285 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001445.pica (DE-627)ELV054566851 (ELSEVIER)S0167-9473(21)00119-5 DE-627 ger DE-627 rakwb eng 610 VZ 540 VZ 35.18 bkl Wang, Pei verfasserin aut Feature filter for estimating central mean subspace and its sparse solution 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. Characteristic function Elsevier Central mean subspace Elsevier Sufficient dimension reduction Elsevier Feature filter Elsevier Yin, Xiangrong oth Yuan, Qingcong oth Kryscio, Richard oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:163 year:2021 pages:0 https://doi.org/10.1016/j.csda.2021.107285 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 163 2021 0 |
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Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. |
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Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. |
| abstract_unstemmed |
Sufficient dimension reduction, replacing the original predictors with a few linear combinations while keeping all the regression information, has been widely studied. A key goal is to find the central mean subspace, the intersection of all subspaces that provide such a reduction. To this end, a new sufficient dimension reduction method is proposed, with two estimation procedures, through a novel approach of feature filter, applicable to both univariate and multivariate responses. Asymptotic results are established. Estimation methods to determine the structural dimension, to obtain sparse estimator and to deal with large p small n data are provided. The efficacy of the method is demonstrated by simulations and a real data example. |
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| title_short |
Feature filter for estimating central mean subspace and its sparse solution |
| url |
https://doi.org/10.1016/j.csda.2021.107285 |
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| author2 |
Yin, Xiangrong Yuan, Qingcong Kryscio, Richard |
| author2Str |
Yin, Xiangrong Yuan, Qingcong Kryscio, Richard |
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| doi_str |
10.1016/j.csda.2021.107285 |
| up_date |
2024-07-06T22:04:55.524Z |
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